文章基本信息

标题：Privacy-Preserving Data Mining based on Integrated Customer Databases from Different Enterprises
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作者：Gábor SZŰCS ; Attila KISS
期刊名称：Economy Informatics
印刷版ISSN：1582-7941
出版年度：2014
卷号：14
期号：1
出版社：INFOREC Association
摘要：The paper is about data mining projects in real applications, where preserving the users' pri- vacy is important. The aim was to build a secure multiparty computation (SMC) data mining system with SMC data mining algorithm that would be able to solve the task of classification in a horizontally distributed environment with multiple parties trying for a joint data mining project. For solution of this kind of privacy preserving problems we have designed and devel- oped an SMC system with different modules, a client module, a trusted third party and a clas- sification module. We have worked out a new classification method; our k-means based su- pervised classifier preserves high level anonymity and provides k-anonymity, where k is a us- er parameter. At the end of the paper a bank example and its results with high accuracy pre- sent the efficiency of our system.
关键词：Anonymity; Customer Databases; Data Mining; Secure Multiparty Computation